The advent of cloud-based computing architectures has opened new possibilities for the rapid and scalable deployment of virtual Web stores, media outlets, social networking sites, and many other on-line sites or services. In general, a cloud-based architecture deploys a set of hosted resources such as processors, operating systems, software and other components that can be combined together to form virtual machines. A user or customer can request the instantiation of a virtual machine or set of machines from those resources from a central server or cloud management system to perform intended tasks, services, or applications. For example, a user may wish to set up and instantiate a virtual server from the cloud to create a storefront to market products or services on a temporary basis, for instance, to sell tickets to or merchandise for an upcoming sports or musical performance. The user can subscribe to the set of resources needed to build and run the set of instantiated virtual machines on a comparatively short-term basis, such as hours or days, for their intended application.
Typically, when a user utilizes a cloud, the user must track the software applications executed in the cloud and/or processes instantiated in the cloud. For example, the user must track the cloud processes to ensure that the correct cloud processes have been instantiated, that the cloud processes are functioning properly and/or efficiently, that the cloud is providing sufficient resources to the cloud processes, and so forth. Due in part to the user's requirements and overall usage of the cloud, the user may have many applications and/or processes instantiated in a cloud at any given instant, and the user's deployment of virtual machines, software, and other resources can change dynamically over time. In cases, the user may also utilize multiple independent clouds to support the user's cloud deployment. That user may further instantiate and use multiple applications or other software or services inside or across multiple of those cloud boundaries, and those resources may be used or consumed by multiple or differing end-user groups in those different cloud networks.
In terms of deployment analysis, a cloud operator or other entity may collect and store the usage history of a user reflecting their consumption of processor, memory, application, and/or other resources in a host or existing cloud. The operator of a further cloud, the user themselves, and/or other entity may wish to explore the possible migration of those subscribed resources to a set of new target clouds. The user or other entity performing that migration analysis may wish to explore those possible deployments on a combinatorial or “mix-and-match” basis, for instance, to achieve the best possible cost ratio for processor and memory, while achieving the greatest possible redundancy for storage capabilities at the same time. When presented with more complex deployment choices, existing cloud tools do not permit or do not assist a user with the task of evaluating a range of possible different cloud-resource combinations over diverse cloud sets, on a comprehensive basis. It may be desirable to provide systems and methods for combinatorial optimization of multiple resources across a set of cloud-based networks, in which cloud-related tools and resources can perform a discovery and evaluation process to determine combinatorially optimized groupings of individual resources and corresponding clouds, to present the user with the best available or preferred combined target choices in their cloud deployment options.